It has become common to use computers to search through large collections of electronic records. In a typical search, a searcher may indicate an interest in some subset of the entire collection using one or more search terms such as words of English or any suitable language. However, such subsets may still be relatively large, particularly where the searcher is a person desiring to review no more than a few records in detail. Furthermore, such subsets may include records perceived by the searcher to be unrelated to the object of the search, for example, because of ambiguities in natural language. As an illustration, if the supplied search term is “bass”, the searcher may be interested in a type of guitar, a type of fish, a brand of beer or something else entirely.
Narrowing the search, for example, by supplying addition search terms, is not necessarily straight forward. When the underlying search space is complex, it is relatively easy for items of interest to be excluded from a search result set by premature search narrowing. For some applications, such premature search narrowing may have significant negative consequences, for example, negative commercial consequences including loss of a sale.
Some conventional search techniques include guided search narrowing. However, each of these techniques has its flaws. For example, some conventional search techniques categorize items of interest and attempt to explicitly associate the search with a particular category. However, manual category association may be laborious and/or fail to avoid premature search narrowing. On the other hand, it may be difficult to achieve sufficient associative confidence, for example, from the context of the search, to enable helpful automatic category association.
Same numbers are used throughout the disclosure and figures to reference like components and features, but such repetition of number is for purposes of simplicity of explanation and understanding, and should not be viewed as a limitation on the various embodiments.